from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


import torch
import torch.nn as nn
import torch._utils
from up.utils.general.log_helper import default_logger as logger
from up.utils.model.normalize import build_norm_layer
from up.utils.general.registry_factory import MODULE_ZOO_REGISTRY


__all__ = ['hrnet18', 'hrnet48', '_hrnet', 'hrnet18_small_v2', 'hrnet18_small_v1']


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, normalize={'type': 'solo_bn'}):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = build_norm_layer(planes, normalize)[1]
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = build_norm_layer(planes, normalize)[1]
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, normalize={'type': 'solo_bn'}):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = build_norm_layer(planes, normalize)[1]
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = build_norm_layer(planes, normalize)[1]
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
                               bias=False)
        self.bn3 = build_norm_layer(planes * self.expansion, normalize)[1]
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True, normalize={'type': 'solo_bn'}):
        super(HighResolutionModule, self).__init__()
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels)

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels, normalize=normalize)
        self.fuse_layers = self._make_fuse_layers(normalize=normalize)
        self.relu = nn.ReLU(inplace=True)

    def _check_branches(self, num_branches, blocks, num_blocks,
                        num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
                num_branches, len(num_blocks))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                num_branches, len(num_channels))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                num_branches, len(num_inchannels))
            logger.error(error_msg)
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                         stride=1, normalize={'type': 'solo_bn'}):
        downsample = None
        if stride != 1 or \
           self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.num_inchannels[branch_index],
                          num_channels[branch_index] * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                build_norm_layer(num_channels[branch_index] * block.expansion, normalize)[1],
            )

        layers = []
        layers.append(block(self.num_inchannels[branch_index],
                            num_channels[branch_index], stride, downsample, normalize=normalize))
        self.num_inchannels[branch_index] = \
            num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(block(self.num_inchannels[branch_index],
                                num_channels[branch_index], normalize=normalize))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels, normalize={'type': 'solo_bn'}):
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels, normalize=normalize))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self, normalize={'type': 'solo_bn'}):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(nn.Sequential(
                        nn.Conv2d(num_inchannels[j],
                                  num_inchannels[i],
                                  1,
                                  1,
                                  0,
                                  bias=False),
                        build_norm_layer(num_inchannels[i], normalize)[1]))
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv3x3s = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                build_norm_layer(num_outchannels_conv3x3, normalize)[1]))
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                build_norm_layer(num_outchannels_conv3x3, normalize)[1],
                                nn.ReLU(inplace=True)))
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []
        for i in range(len(self.fuse_layers)):
            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
            for j in range(1, self.num_branches):
                if i == j:
                    y = y + x[j]
                elif j > i:
                    width_output = x[i].shape[-1]
                    height_output = x[i].shape[-2]
                    upsample = nn.UpsamplingBilinear2d(size=(height_output, width_output))
                    # y = y + F.interpolate(
                    #     self.fuse_layers[i][j](x[j]),
                    #     size=[height_output, width_output],
                    #     mode='bilinear')
                    y = y + upsample(self.fuse_layers[i][j](x[j]))
                else:
                    y = y + self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))

        return x_fuse


blocks_dict = {
    'basic': BasicBlock,
    'bottleneck': Bottleneck
}


class HighResolutionNet(nn.Module):

    def __init__(self, stages, normalize={'type': 'solo_bn'}):
        extra = stages
        super(HighResolutionNet, self).__init__()
        # stem net
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn1 = build_norm_layer(64, normalize)[1]
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn2 = build_norm_layer(64, normalize)[1]
        self.relu = nn.ReLU(inplace=True)

        self.stage1_cfg = extra['stage1']
        num_channels = self.stage1_cfg['num_channels'][0]
        block = blocks_dict[self.stage1_cfg['block']]
        num_blocks = self.stage1_cfg['num_blocks'][0]
        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks, normalize=normalize)
        stage1_out_channel = block.expansion * num_channels

        self.stage2_cfg = extra['stage2']
        num_channels = self.stage2_cfg['num_channels']
        block = blocks_dict[self.stage2_cfg['block']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition1 = self._make_transition_layer(
            [stage1_out_channel], num_channels, normalize=normalize)
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels, normalize=normalize)

        self.stage3_cfg = extra['stage3']
        num_channels = self.stage3_cfg['num_channels']
        block = blocks_dict[self.stage3_cfg['block']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_channels, normalize=normalize)
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels, normalize=normalize)

        self.stage4_cfg = extra['stage4']
        num_channels = self.stage4_cfg['num_channels']
        block = blocks_dict[self.stage4_cfg['block']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_channels, normalize=normalize)
        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg, num_channels, multi_scale_output=True, normalize=normalize)

        # last_inp_channels = np.int(np.sum(pre_stage_channels))
        self.pre_stage_channels = pre_stage_channels

    def _make_transition_layer(
            self, num_channels_pre_layer, num_channels_cur_layer, normalize={'type': 'solo_bn'}):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(nn.Sequential(
                        nn.Conv2d(num_channels_pre_layer[i],
                                  num_channels_cur_layer[i],
                                  3,
                                  1,
                                  1,
                                  bias=False),
                        build_norm_layer(num_channels_cur_layer[i], normalize)[1],
                        nn.ReLU(inplace=True)))
                else:
                    transition_layers.append(None)
            else:
                conv3x3s = []
                for j in range(i + 1 - num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = num_channels_cur_layer[i] \
                        if j == i - num_branches_pre else inchannels
                    conv3x3s.append(nn.Sequential(nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
                                    build_norm_layer(outchannels, normalize)[1], nn.ReLU(inplace=True)))
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    def _make_layer(self, block, inplanes, planes, blocks, stride=1, normalize={'type': 'solo_bn'}):
        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = nn.Sequential(nn.Conv2d(inplanes, planes * block.expansion,
                                       kernel_size=1, stride=stride, bias=False),
                                       build_norm_layer(planes * block.expansion, normalize)[1])

        layers = []
        layers.append(block(inplanes, planes, stride, downsample, normalize=normalize))
        inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(inplanes, planes, normalize=normalize))

        return nn.Sequential(*layers)

    def _make_stage(self, layer_config, num_inchannels,
                    multi_scale_output=True, normalize={'type': 'solo_bn'}):
        num_modules = layer_config['num_modules']
        num_branches = layer_config['num_branches']
        num_blocks = layer_config['num_blocks']
        num_channels = layer_config['num_channels']
        block = blocks_dict[layer_config['block']]
        fuse_method = layer_config['fuse_method']

        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True
            modules.append(
                HighResolutionModule(num_branches,
                                     block,
                                     num_blocks,
                                     num_inchannels,
                                     num_channels,
                                     fuse_method,
                                     reset_multi_scale_output,
                                     normalize=normalize)
            )
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def get_outplanes(self):
        return self.pre_stage_channels

    def forward(self, input):
        img = input["image"]
        size = img.size()[2:]
        x = self.conv1(img)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.layer1(x)

        x_list = []
        for i in range(self.stage2_cfg['num_branches']):
            if self.transition1[i] is not None:
                x_list.append(self.transition1[i](x))
            else:
                x_list.append(x)
        y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.stage3_cfg['num_branches']):
            if self.transition2[i] is not None:
                x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage3(x_list)

        x_list = []
        for i in range(self.stage4_cfg['num_branches']):
            if self.transition3[i] is not None:
                x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        x = self.stage4(x_list)

        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        # x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear')
        # x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear')
        # x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear')
        upsample = nn.UpsamplingBilinear2d(size=(x0_h, x0_w))
        x1 = upsample(x[1])
        x2 = upsample(x[2])
        x3 = upsample(x[3])

        feats = torch.cat([x[0], x1, x2, x3], 1)

        res = {}
        res["features"] = feats
        res["size"] = size
        res['image'] = img  # for biseg net like network
        return res


@MODULE_ZOO_REGISTRY.register('hrnet')
def _hrnet(**kwargs):

    model = HighResolutionNet(**kwargs)
    return model


@MODULE_ZOO_REGISTRY.register('hrnet18')
def hrnet18(**kwargs):
    r"""HRNet-18 model
    """
    stages = {
        "stage1": {
            'num_modules': 1,
            'num_branches': 1,
            'block': 'bottleneck',
            'num_blocks': [4],
            'num_channels': [64],
            'fuse_method': 'sum'
        },
        "stage2": {
            'num_modules': 1,
            'num_branches': 2,
            'block': 'basic',
            'num_blocks': [4, 4],
            'num_channels': [18, 36],
            'fuse_method': 'sum'
        },
        "stage3": {
            'num_modules': 4,
            'num_branches': 3,
            'block': 'basic',
            'num_blocks': [4, 4, 4],
            'num_channels': [18, 36, 72],
            'fuse_method': 'sum'
        },
        "stage4": {
            'num_modules': 3,
            'num_branches': 4,
            'block': 'basic',
            'num_blocks': [4, 4, 4, 4],
            'num_channels': [18, 36, 72, 144],
            'fuse_method': 'sum'
        },

    }
    return HighResolutionNet(stages, **kwargs)


@MODULE_ZOO_REGISTRY.register('hrnet48')
def hrnet48(**kwargs):
    stages = {
        "stage1": {
            'num_modules': 1,
            'num_branches': 1,
            'block': 'bottleneck',
            'num_blocks': [4],
            'num_channels': [64],
            'fuse_method': 'sum'
        },
        "stage2": {
            'num_modules': 1,
            'num_branches': 2,
            'block': 'basic',
            'num_blocks': [4, 4],
            'num_channels': [48, 96],
            'fuse_method': 'sum'
        },
        "stage3": {
            'num_modules': 4,
            'num_branches': 3,
            'block': 'basic',
            'num_blocks': [4, 4, 4],
            'num_channels': [48, 96, 192],
            'fuse_method': 'sum'
        },
        "stage4": {
            'num_modules': 3,
            'num_branches': 4,
            'block': 'basic',
            'num_blocks': [4, 4, 4, 4],
            'num_channels': [48, 96, 192, 384],
            'fuse_method': 'sum'
        }
    }
    return HighResolutionNet(stages, **kwargs)


@MODULE_ZOO_REGISTRY.register('hrnet18_small_v1')
def hrnet18_small_v1(**kwargs):
    stages = {
        "stage1": {
            'num_modules': 1,
            'num_branches': 1,
            'block': 'bottleneck',
            'num_blocks': [1],
            'num_channels': [32],
            'fuse_method': 'sum'
        },
        "stage2": {
            'num_modules': 1,
            'num_branches': 2,
            'block': 'basic',
            'num_blocks': [2, 2],
            'num_channels': [16, 32],
            'fuse_method': 'sum'
        },
        "stage3": {
            'num_modules': 1,
            'num_branches': 3,
            'block': 'basic',
            'num_blocks': [2, 2, 2],
            'num_channels': [16, 32, 64],
            'fuse_method': 'sum'
        },
        "stage4": {
            'num_modules': 1,
            'num_branches': 4,
            'block': 'basic',
            'num_blocks': [2, 2, 2, 2],
            'num_channels': [16, 32, 64, 128],
            'fuse_method': 'sum'
        },

    }
    return HighResolutionNet(stages, **kwargs)


@MODULE_ZOO_REGISTRY.register('hrnet18_small_v2')
def hrnet18_small_v2(**kwargs):
    stages = {
        "stage1": {
            'num_modules': 1,
            'num_branches': 1,
            'block': 'bottleneck',
            'num_blocks': [2],
            'num_channels': [64],
            'fuse_method': 'sum'
        },
        "stage2": {
            'num_modules': 1,
            'num_branches': 2,
            'block': 'basic',
            'num_blocks': [2, 2],
            'num_channels': [18, 36],
            'fuse_method': 'sum'
        },
        "stage3": {
            'num_modules': 3,
            'num_branches': 3,
            'block': 'basic',
            'num_blocks': [2, 2, 2],
            'num_channels': [18, 36, 72],
            'fuse_method': 'sum'
        },
        "stage4": {
            'num_modules': 2,
            'num_branches': 4,
            'block': 'basic',
            'num_blocks': [2, 2, 2, 2],
            'num_channels': [18, 36, 72, 144],
            'fuse_method': 'sum'
        },

    }
    return HighResolutionNet(stages, **kwargs)
